EasySegmentSupervisedTrain

Support
Required licenses	EasySegment
Recommended images	n/a
Location	Deep Learning Inspection/EasySegmentSupervisedTrain

Purpose

This sample program demonstrates how to:

• Train an EasySegment Supervised tool in console mode.

Code highlights

By default, the sample program trains a tool for the dataset Coffee in the Deep Learning Additional Resources.

Note

In the code, you need to replace DEEP_LEARNING_ADDITIONAL_RESOURCES by the path of the Deep Learning Additional Resources.

1. Select the first available GPU for the training.

   //initialize a segmenter that will be trained
   ESupervisedSegmenter newSupervisedSegmenter;
   
   // make sure that we have a GPU to use (note: GPU's can only be detected in x64 configuration)
   std::vector<EDeepLearningDevice> devices = newClassifier.GetAvailableDevices();
   
   bool foundGPU = false;
   for (size_t i = 0; i < devices.size(); i++)
   {
     if (devices[i].GetDeviceType() == EDeepLearningDeviceType_GPU)
     {
       newClassifier.SetActiveDevice(devices[i]);
       foundGPU = true;
       break;
     }
   }
   

2. Load a dataset from a project.

   const EClassificationDataset* dataset;
   EDeepLearningProject project;
   project.Load(PROJECT_PATH);
   dataset = &project.GetDataset();
   

3. Alternatively, load a dataset directly using a .edldataset file.

   const EClassificationDataset* dataset;
   EClassificationDataset datasetHolder;
   datasetHolder.Load(DATASET_PATH);
   dataset = &datasetHolder;
   

4. Split the dataset in a training part and a validation part.

Note

Be careful that the method for splitting a dataset is different for each type of tool.

```c++
// Put 80% of images into the training dataset and 20% in the validation dataset
EClassificationDataset trainingDataset;
EClassificationDataset validationDataset;
dataset->SplitDatasetForSegmentation(trainingDataset, validationDataset, 0.8f);
```

5. Train the tool and wait for the training to end.

   newSupervisedSegmenter.Train(trainingDataset, validationDataset, 5);
   newSupervisedSegmenter.WaitForTrainingCompletion();
   

6. Check the validation IoU (intersection over union) of the tool we just trained.

   float newSupervisedSegmenterIoU = newSupervisedSegmenter.GetValidationMetrics(newSupervisedSegmenter.GetBestIteration()).GetWeightedIntersectionOverUnion();
   std::cout << "New segmenter IoU: " << newSupervisedSegmenterIoU << std::endl;